Mediation and Moderation Models with R

PREVIOUS STEPS

Before sourcing file process.R, you need to download it at the folder in which you’ve saved the Rmd file. The R script is available at this link.

pkgs2use<- c('haven','tidyverse','here','DiagrammeR','interactions')

inst.load.pkg <- function(pkg){ 
  if (!require(pkg, character.only = TRUE)) {
      install.packages(pkg, dependencies = TRUE)
      library(pkg, character.only = TRUE)
  }
}


invisible(lapply(pkgs2use,inst.load.pkg))
rm(pkgs2use,inst.load.pkg)

source(here("Complementos","MedModR","MATERIALS","Practice","process.R"))

********************* PROCESS for R Version 4.1.1 ********************* 
 
           Written by Andrew F. Hayes, Ph.D.  www.afhayes.com              
   Documentation available in Hayes (2022). www.guilford.com/p/hayes3   
 
*********************************************************************** 
 
PROCESS is now ready for use.
Copyright 2022 by Andrew F. Hayes ALL RIGHTS RESERVED
Workshop schedule at http://haskayne.ucalgary.ca/CCRAM
 

SIMPLE MEDIATION MODELS WITH R

Import dataset

StressDF <- read_sav(here("Complementos","MedModR","MATERIALS","Practice","DataSets", "estress.sav"))

Fit model

results <- process(StressDF,y='withdraw',x='estress',m='affect',total=1,model=4,progress=0,save=3)

********************* PROCESS for R Version 4.1.1 ********************* 
 
           Written by Andrew F. Hayes, Ph.D.  www.afhayes.com              
   Documentation available in Hayes (2022). www.guilford.com/p/hayes3   
 
*********************************************************************** 
                
Model : 4       
    Y : withdraw
    X : estress 
    M : affect  

Sample size: 262

Random seed: 827757


*********************************************************************** 
Outcome Variable: affect

Model Summary: 
          R      R-sq       MSE         F       df1       df2         p
     0.3401    0.1156    0.4650   33.9988    1.0000  260.0000    0.0000

Model: 
             coeff        se         t         p      LLCI      ULCI
constant    0.7994    0.1433    5.5777    0.0000    0.5172    1.0816
estress     0.1729    0.0296    5.8308    0.0000    0.1145    0.2313

*********************************************************************** 
Outcome Variable: withdraw

Model Summary: 
          R      R-sq       MSE         F       df1       df2         p
     0.4247    0.1804    1.2841   28.4946    2.0000  259.0000    0.0000

Model: 
             coeff        se         t         p      LLCI      ULCI
constant    1.4471    0.2520    5.7420    0.0000    0.9508    1.9433
estress    -0.0768    0.0524   -1.4667    0.1437   -0.1800    0.0263
affect      0.7691    0.1031    7.4627    0.0000    0.5662    0.9721

************************ TOTAL EFFECT MODEL *************************** 
Outcome Variable: withdraw

Model Summary: 
          R      R-sq       MSE         F       df1       df2         p
     0.0641    0.0041    1.5543    1.0718    1.0000  260.0000    0.3015

Model: 
             coeff        se         t         p      LLCI      ULCI
constant    2.0619    0.2620    7.8691    0.0000    1.5459    2.5778
estress     0.0561    0.0542    1.0353    0.3015   -0.0506    0.1629

*********************************************************************** 
Bootstrapping in progress. Please wait.

************ TOTAL, DIRECT, AND INDIRECT EFFECTS OF X ON Y ************

Total effect of X on Y:
     effect        se         t         p      LLCI      ULCI
     0.0561    0.0542    1.0353    0.3015   -0.0506    0.1629

Direct effect of X on Y:
     effect        se         t         p      LLCI      ULCI
    -0.0768    0.0524   -1.4667    0.1437   -0.1800    0.0263

Indirect effect(s) of X on Y:
          Effect    BootSE  BootLLCI  BootULCI
affect    0.1330    0.0340    0.0709    0.2034

*********************************************************************** 

Map of column names to model coefficients in bootstrap matrix:
  name Conseqnt Antecdnt
  col1   affect constant
  col2   affect  estress
  col3 withdraw constant
  col4 withdraw  estress
  col5 withdraw   affect

******************** ANALYSIS NOTES AND ERRORS ************************ 

Level of confidence for all confidence intervals in output: 95

Number of bootstraps for percentile bootstrap confidence intervals: 5000

Plot fitted model

resCOEFS <- round(results[[2]],2)

starlabs <- c(paste("c´=",resCOEFS[12,1],"<FONT POINT-SIZE='6'><SUP>",ifelse(resCOEFS[12,4]<.001,'***',ifelse(resCOEFS[12,4]<.01,'**',ifelse(resCOEFS[12,4]<.05,'*',' '))),"</SUP></FONT>",sep=''),
              paste("a<FONT POINT-SIZE='6'><SUB>1</SUB></FONT>=",resCOEFS[3,1],"<FONT POINT-SIZE='6'><SUP>",ifelse(resCOEFS[3,4]<.001,'***',ifelse(resCOEFS[3,4]<.01,'**',ifelse(resCOEFS[3,4]<.05,'*',' '))),"</SUP></FONT>",sep=''),
              paste("b<FONT POINT-SIZE='6'><SUB>1</SUB></FONT>=",resCOEFS[7,1],"<FONT POINT-SIZE='6'><SUP>",ifelse(resCOEFS[7,4]<.001,'***',ifelse(resCOEFS[7,4]<.01,'**',ifelse(resCOEFS[7,4]<.05,'*',' '))),"</SUP></FONT>",sep=''),
              paste("a<FONT POINT-SIZE='6'><SUB>2</SUB></FONT>=",resCOEFS[6,1],"<FONT POINT-SIZE='6'><SUP>",ifelse(resCOEFS[6,4]<.001,'***',ifelse(resCOEFS[6,4]<.01,'**',ifelse(resCOEFS[6,4]<.05,'*',' '))),"</SUP></FONT>",sep='')
              )
grViz(paste("
      digraph SEM {
      
      graph [layout = neato,
      overlap = true,
      outputorder = edgesfirst
      title='TF']
      
      node [shape = rectangle,fontsize=10]
      
      
      a [pos = '-1,0!', label = 'STRESS']
      b [pos = '1,1!', label = 'AFFECT']
      c [pos = '3,0!', label = 'WITHDRAWAL']

      node [shape=plaintext,fontsize=9.5]
      
      ac [pos='1,.1!', label = <",starlabs[1],">]
      ab [pos='-0.2,.6!', label = <",starlabs[2],">]
      bc [pos='2.2,.6!', label = <",starlabs[3],">]
      
      a->b
      a->c
      b->c
}
",sep='')
)

MULTIPLE MEDIATION MODELS WITH R

Import dataset

pmiDF <- read_sav(here("Complementos","MedModR","MATERIALS","Practice","DataSets", "pmi.sav"))

Fit model

results <- process(pmiDF,y='reaction',x='cond',m=c('pmi','import'),total=1,model=4,save=3,progress=0)

********************* PROCESS for R Version 4.1.1 ********************* 
 
           Written by Andrew F. Hayes, Ph.D.  www.afhayes.com              
   Documentation available in Hayes (2022). www.guilford.com/p/hayes3   
 
*********************************************************************** 
                
Model : 4       
    Y : reaction
    X : cond    
   M1 : pmi     
   M2 : import  

Sample size: 123

Random seed: 461623


*********************************************************************** 
Outcome Variable: pmi

Model Summary: 
          R      R-sq       MSE         F       df1       df2         p
     0.1808    0.0327    1.7026    4.0878    1.0000  121.0000    0.0454

Model: 
             coeff        se         t         p      LLCI      ULCI
constant    5.3769    0.1618   33.2222    0.0000    5.0565    5.6973
cond        0.4765    0.2357    2.0218    0.0454    0.0099    0.9431

*********************************************************************** 
Outcome Variable: import

Model Summary: 
          R      R-sq       MSE         F       df1       df2         p
     0.1809    0.0327    2.9411    4.0942    1.0000  121.0000    0.0452

Model: 
             coeff        se         t         p      LLCI      ULCI
constant    3.9077    0.2127   18.3704    0.0000    3.4866    4.3288
cond        0.6268    0.3098    2.0234    0.0452    0.0135    1.2401

*********************************************************************** 
Outcome Variable: reaction

Model Summary: 
          R      R-sq       MSE         F       df1       df2         p
     0.5702    0.3251    1.6628   19.1118    3.0000  119.0000    0.0000

Model: 
             coeff        se         t         p      LLCI      ULCI
constant   -0.1498    0.5298   -0.2828    0.7778   -1.1989    0.8993
cond        0.1034    0.2391    0.4324    0.6662   -0.3701    0.5768
pmi         0.3965    0.0930    4.2645    0.0000    0.2124    0.5806
import      0.3244    0.0707    4.5857    0.0000    0.1843    0.4645

************************ TOTAL EFFECT MODEL *************************** 
Outcome Variable: reaction

Model Summary: 
          R      R-sq       MSE         F       df1       df2         p
     0.1603    0.0257    2.3610    3.1897    1.0000  121.0000    0.0766

Model: 
             coeff        se         t         p      LLCI      ULCI
constant    3.2500    0.1906   17.0525    0.0000    2.8727    3.6273
cond        0.4957    0.2775    1.7860    0.0766   -0.0538    1.0452

*********************************************************************** 
Bootstrapping in progress. Please wait.

************ TOTAL, DIRECT, AND INDIRECT EFFECTS OF X ON Y ************

Total effect of X on Y:
     effect        se         t         p      LLCI      ULCI
     0.4957    0.2775    1.7860    0.0766   -0.0538    1.0452

Direct effect of X on Y:
     effect        se         t         p      LLCI      ULCI
     0.1034    0.2391    0.4324    0.6662   -0.3701    0.5768

Indirect effect(s) of X on Y:
          Effect    BootSE  BootLLCI  BootULCI
TOTAL     0.3923    0.1642    0.0925    0.7350
pmi       0.1890    0.1035    0.0063    0.4154
import    0.2033    0.1134    0.0072    0.4564

*********************************************************************** 

Map of column names to model coefficients in bootstrap matrix:
  name Conseqnt Antecdnt
  col1      pmi constant
  col2      pmi     cond
  col3   import constant
  col4   import     cond
  col5 reaction constant
  col6 reaction     cond
  col7 reaction      pmi
  col8 reaction   import

******************** ANALYSIS NOTES AND ERRORS ************************ 

Level of confidence for all confidence intervals in output: 95

Number of bootstraps for percentile bootstrap confidence intervals: 5000

Plot fitted model

resCOEFS <- round(results[[2]],2)

starlabs <- c(paste("c´=",resCOEFS[9,1],"<FONT POINT-SIZE='6'><SUP>",ifelse(resCOEFS[9,4]<.001,'***',ifelse(resCOEFS[9,4]<.01,'**',ifelse(resCOEFS[9,4]<=.05,'*',' '))),"</SUP></FONT>",sep=''),
              paste("a<FONT POINT-SIZE='6'><SUB>1</SUB></FONT>=",resCOEFS[6,1],"<FONT POINT-SIZE='6'><SUP>",ifelse(resCOEFS[6,4]<.001,'***',ifelse(resCOEFS[6,4]<.01,'**',ifelse(resCOEFS[6,4]<=.05,'*',' '))),"</SUP></FONT>",sep=''),
              paste("b<FONT POINT-SIZE='6'><SUB>1</SUB></FONT>=",resCOEFS[11,1],"<FONT POINT-SIZE='6'><SUP>",ifelse(resCOEFS[11,4]<.001,'***',ifelse(resCOEFS[11,4]<.01,'**',ifelse(resCOEFS[11,4]<=.05,'*',' '))),"</SUP></FONT>",sep=''),
              paste("a<FONT POINT-SIZE='6'><SUB>2</SUB></FONT>=",resCOEFS[3,1],"<FONT POINT-SIZE='6'><SUP>",ifelse(resCOEFS[3,4]<.001,'***',ifelse(resCOEFS[3,4]<.01,'**',ifelse(resCOEFS[3,4]<=.05,'*',' '))),"</SUP></FONT>",sep=''),
              paste("b<FONT POINT-SIZE='6'><SUB>2</SUB></FONT>=",resCOEFS[10,1],"<FONT POINT-SIZE='6'><SUP>",ifelse(resCOEFS[10,4]<.001,'***',ifelse(resCOEFS[10,4]<.01,'**',ifelse(resCOEFS[10,4]<=.05,'*',' '))),"</SUP></FONT>",sep='')              
              )
grViz(paste("
      digraph SEM {
      
      graph [layout = neato,
      overlap = true,
      outputorder = edgesfirst
      title='TF']
      
      node [shape = rectangle,fontsize=10]
      
      
      a [pos = '-1,0!', label = 'CONDITION']
      b [pos = '1,1!', label = 'IMPORT']
      c [pos = '3,0!', label = 'REACTION']
      d [pos = '1,-1!', label = 'PMI']

      node [shape=plaintext,fontsize=9.5]
      
      ac [pos='1,.1!', label = <",starlabs[1],">]
      ab [pos='-0.2,.6!', label = <",starlabs[2],">]
      bc [pos='2.2,.6!', label = <",starlabs[3],">]
      ad [pos='0.3,-.4!', label = <",starlabs[4],">]
      dc [pos='1.8,-.4!', label = <",starlabs[5],">]
      
      a->b
      a->c
      b->c
      a->d
      d->c
}
",sep='')
)

MODERATION MODELS WITH R

Import dataset

disasterDF <- read_sav(here("Complementos","MedModR","MATERIALS","Practice","DataSets", "disaster.sav"))

Fit Model

process(disasterDF,x='frame',y='justify',w='skeptic',model=1,jn=1)

********************* PROCESS for R Version 4.1.1 ********************* 
 
           Written by Andrew F. Hayes, Ph.D.  www.afhayes.com              
   Documentation available in Hayes (2022). www.guilford.com/p/hayes3   
 
*********************************************************************** 
               
Model : 1      
    Y : justify
    X : frame  
    W : skeptic

Sample size: 211


*********************************************************************** 
Outcome Variable: justify

Model Summary: 
          R      R-sq       MSE         F       df1       df2         p
     0.4962    0.2463    0.6609   22.5430    3.0000  207.0000    0.0000

Model: 
             coeff        se         t         p      LLCI      ULCI
constant    2.4515    0.1490   16.4486    0.0000    2.1577    2.7454
frame      -0.5625    0.2179   -2.5811    0.0105   -0.9921   -0.1328
skeptic     0.1051    0.0381    2.7559    0.0064    0.0299    0.1803
Int_1       0.2012    0.0553    3.6401    0.0003    0.0922    0.3101

Product terms key:
Int_1  :  frame  x  skeptic      

Test(s) of highest order unconditional interaction(s):
      R2-chng         F       df1       df2         p
X*W    0.0482   13.2503    1.0000  207.0000    0.0003
----------
Focal predictor: frame (X)
      Moderator: skeptic (W)

Conditional effects of the focal predictor at values of the moderator(s):
    skeptic    effect        se         t         p      LLCI      ULCI
     1.5920   -0.2422    0.1495   -1.6203    0.1067   -0.5369    0.0525
     2.8000    0.0008    0.1166    0.0072    0.9942   -0.2290    0.2307
     5.2000    0.4837    0.1505    3.2134    0.0015    0.1869    0.7804

Moderator value(s) defining Johnson-Neyman significance region(s):
      Value   % below   % above
     1.1713    6.6351   93.3649
     3.9339   67.7725   32.2275

Conditional effect of focal predictor at values of the moderator:
    skeptic    effect        se         t         p      LLCI      ULCI
     1.0000   -0.3613    0.1729   -2.0900    0.0378   -0.7021   -0.0205
     1.1713   -0.3268    0.1658   -1.9715    0.0500   -0.6536    0.0000
     1.4211   -0.2766    0.1559   -1.7743    0.0775   -0.5839    0.0307
     1.8421   -0.1919    0.1407   -1.3637    0.1742   -0.4693    0.0855
     2.2632   -0.1072    0.1280   -0.8373    0.4034   -0.3595    0.1451
     2.6842   -0.0225    0.1185   -0.1895    0.8499   -0.2561    0.2112
     3.1053    0.0623    0.1131    0.5505    0.5826   -0.1607    0.2852
     3.5263    0.1470    0.1123    1.3082    0.1923   -0.0745    0.3684
     3.9339    0.2290    0.1161    1.9715    0.0500    0.0000    0.4579
     3.9474    0.2317    0.1163    1.9914    0.0477    0.0023    0.4610
     4.3684    0.3164    0.1246    2.5388    0.0119    0.0707    0.5621
     4.7895    0.4011    0.1364    2.9401    0.0037    0.1321    0.6700
     5.2105    0.4858    0.1509    3.2192    0.0015    0.1883    0.7833
     5.6316    0.5705    0.1674    3.4083    0.0008    0.2405    0.9005
     6.0526    0.6552    0.1853    3.5354    0.0005    0.2898    1.0206
     6.4737    0.7399    0.2044    3.6208    0.0004    0.3370    1.1428
     6.8947    0.8246    0.2242    3.6785    0.0003    0.3827    1.2666
     7.3158    0.9093    0.2446    3.7176    0.0003    0.4271    1.3916
     7.7368    0.9941    0.2655    3.7440    0.0002    0.4706    1.5175
     8.1579    1.0788    0.2868    3.7617    0.0002    0.5134    1.6441
     8.5789    1.1635    0.3083    3.7735    0.0002    0.5556    1.7713
     9.0000    1.2482    0.3301    3.7810    0.0002    0.5974    1.8990

******************** ANALYSIS NOTES AND ERRORS ************************ 

Level of confidence for all confidence intervals in output: 95

W values in conditional tables are the 16th, 50th, and 84th percentiles.

Plot moderation

mod.moderation <- lm(justify~frame*skeptic,data=disasterDF)
interactions::johnson_neyman(model = mod.moderation, pred = frame,modx = skeptic)

JOHNSON-NEYMAN INTERVAL 

When skeptic is OUTSIDE the interval [1.17, 3.93], the slope of frame is p
< .05.

Note: The range of observed values of skeptic is [1.00, 9.00]